Dynamical Modeling Methods for Systems Biology

开始时间: 04/22/2022 持续时间: 7 weeks

所在平台: CourseraArchive 课程类别: 数学 大学或机构: Icahn School of Medicine at Mount Sinai(西奈山伊坎医学院) 授课老师： Eric Sobie

课程主页: https://www.coursera.org/course/dynamicalmodeling

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课程详情

We take a case-based approach to teach contemporary mathematical modeling techniques.  The course is appropriate for advanced undergraduates and beginning graduate students.  Lectures provide biological background and describe the development of both classical mathematical models and more recent representations of biological processes.  The course will be useful for students who plan to use experimental techniques as their approach in the laboratory and employ computational modeling as a tool to draw deeper understanding of experiments. The course should also be valuable as an introductory overview for students planning to conduct original research in modeling biological systems.

This course focuses on dynamical modeling techniques used in Systems Biology research.  These techniques are based on biological mechanisms, and simulations with these models generate predictions that can subsequently be tested experimentally. These testable predictions frequently provide novel insight into biological processes.  The approaches taught here can be grouped into the following categories:  1) ordinary differential equation-based models, 2) partial differential equation-based models, and 3) stochastic models. 

课程大纲

Topics covered include:

• Modeling in Systems Biology - ODE Models
• Modeling in Systems Biology - PDE Models
• Computing with Matlab
• Computing with Octave
• Introduction to Dynamical Systems
• Developing Models: Extracting Constants from Experimental Literature
• Modeling Emergent Properties: Bistability in Biochemical Signaling I
• Modeling Emergent Properties: Bistability in Biochemical Signaling II
• Modeling with Cell Cycle with Systems of ODEs Cycle
• ODE Model of the Action Potential
• Parameter Sensitivity Analysis to assess Robustness and Fragility
• Curve Fitting and Error Estimation
• PDE Models of Propagating Action Potentials
• PDE Models of Vesicle Transport within Cells
• Modeling in Virtual Cell: ODEs and PDEs
• PDE Models of Live Cell Imaging Experiments: Predictions and Verification
• Introduction to Stochastic Models for Cell Biological Processes
• Stochastic Modeling of Biochemical Processes: Master Equation-Gillespie Algorithm
• Stochastic Model of Neurotransmitter Release
• Stochastic Model of Transcription Initiation

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